Overview

Dataset statistics

Number of variables9
Number of observations366
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.6 KiB
Average record size in memory80.0 B

Variable types

Numeric9

Alerts

FREQUENCIA BOMBA 1 is highly overall correlated with FREQUENCIA BOMBA 2 and 7 other fieldsHigh correlation
NIVEL DO RESERVATÓRIO - LT01 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
VAZÃO DE ENTRADA- FT01 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
VAZÃO DE GRAVIDADE - FT02 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
VAZÃO DE RECALQUE - FT03 is highly overall correlated with FREQUENCIA BOMBA 1 and 7 other fieldsHigh correlation
PRESSÃO DE SUCÇÃO - PT01 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
PRESSÃO DE RECALQUE - PT02 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
FREQUENCIA BOMBA 2 is highly overall correlated with FREQUENCIA BOMBA 1 and 7 other fieldsHigh correlation
FREQUENCIA BOMBA 3 is highly overall correlated with FREQUENCIA BOMBA 1 and 2 other fieldsHigh correlation
NIVEL DO RESERVATÓRIO - LT01 has unique valuesUnique
VAZÃO DE ENTRADA- FT01 has unique valuesUnique
VAZÃO DE GRAVIDADE - FT02 has unique valuesUnique
VAZÃO DE RECALQUE - FT03 has unique valuesUnique
PRESSÃO DE SUCÇÃO - PT01 has unique valuesUnique
PRESSÃO DE RECALQUE - PT02 has unique valuesUnique
FREQUENCIA BOMBA 1 has 8 (2.2%) zerosZeros
FREQUENCIA BOMBA 2 has 70 (19.1%) zerosZeros
FREQUENCIA BOMBA 3 has 227 (62.0%) zerosZeros

Reproduction

Analysis started2022-12-14 16:00:32.730304
Analysis finished2022-12-14 16:00:56.329671
Duration23.6 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

FREQUENCIA BOMBA 1
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct356
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.932082
Minimum0
Maximum57.884742
Zeros8
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T13:00:56.493621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.3437257
Q133.595225
median52.689572
Q354.726129
95-th percentile56.667869
Maximum57.884742
Range57.884742
Interquartile range (IQR)21.130904

Descriptive statistics

Standard deviation17.624569
Coefficient of variation (CV)0.42031228
Kurtosis-0.25952149
Mean41.932082
Median Absolute Deviation (MAD)3.6242802
Skewness-1.0810598
Sum15347.142
Variance310.62544
MonotonicityNot monotonic
2022-12-14T13:00:56.735546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
2.2%
10.45626259 2
 
0.5%
6.707071304 2
 
0.5%
16.33021418 2
 
0.5%
53.90830898 1
 
0.3%
0.07812268396 1
 
0.3%
34.78656371 1
 
0.3%
7.864561024 1
 
0.3%
37.20146571 1
 
0.3%
57.67244562 1
 
0.3%
Other values (346) 346
94.5%
ValueCountFrequency (%)
0 8
2.2%
0.07812268396 1
 
0.3%
0.1135240048 1
 
0.3%
1.457959493 1
 
0.3%
1.458722432 1
 
0.3%
2.138453166 1
 
0.3%
2.416199684 1
 
0.3%
5.207150777 1
 
0.3%
5.62386322 1
 
0.3%
5.790993781 1
 
0.3%
ValueCountFrequency (%)
57.88474162 1
0.3%
57.80188862 1
0.3%
57.78695647 1
0.3%
57.72492854 1
0.3%
57.67244562 1
0.3%
57.64668687 1
0.3%
57.41708151 1
0.3%
57.24754477 1
0.3%
57.23739465 1
0.3%
57.12615188 1
0.3%

FREQUENCIA BOMBA 2
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct297
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.78064
Minimum0
Maximum53.488396
Zeros70
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T13:00:56.987470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.3856964
median16.350328
Q323.106649
95-th percentile32.569798
Maximum53.488396
Range53.488396
Interquartile range (IQR)16.720952

Descriptive statistics

Standard deviation11.487194
Coefficient of variation (CV)0.7279295
Kurtosis0.46790366
Mean15.78064
Median Absolute Deviation (MAD)7.6099457
Skewness0.46529606
Sum5775.7143
Variance131.95562
MonotonicityNot monotonic
2022-12-14T13:00:57.241392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
 
19.1%
16.29283444 1
 
0.3%
12.2011315 1
 
0.3%
13.17303356 1
 
0.3%
11.47718573 1
 
0.3%
12.5148557 1
 
0.3%
21.22075748 1
 
0.3%
11.57950465 1
 
0.3%
7.84309721 1
 
0.3%
26.73429219 1
 
0.3%
Other values (287) 287
78.4%
ValueCountFrequency (%)
0 70
19.1%
1.041552226 1
 
0.3%
1.249832153 1
 
0.3%
1.379900932 1
 
0.3%
1.458112081 1
 
0.3%
2.101109505 1
 
0.3%
2.189982891 1
 
0.3%
2.697555224 1
 
0.3%
2.707944234 1
 
0.3%
2.811360359 1
 
0.3%
ValueCountFrequency (%)
53.48839553 1
0.3%
52.76372433 1
0.3%
52.23364218 1
0.3%
50.31093295 1
0.3%
50.30937306 1
0.3%
50.05674442 1
0.3%
47.95462648 1
0.3%
47.28153245 1
0.3%
46.25633566 1
0.3%
46.10485204 1
0.3%

FREQUENCIA BOMBA 3
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct133
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2133666
Minimum0
Maximum46.841334
Zeros227
Zeros (%)62.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T13:00:57.519306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38.6363372
95-th percentile30.022477
Maximum46.841334
Range46.841334
Interquartile range (IQR)8.6363372

Descriptive statistics

Standard deviation10.851065
Coefficient of variation (CV)1.7464067
Kurtosis1.5582587
Mean6.2133666
Median Absolute Deviation (MAD)0
Skewness1.6685356
Sum2274.0922
Variance117.74562
MonotonicityNot monotonic
2022-12-14T13:00:57.827214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 227
62.0%
9.664798737 5
 
1.4%
7.248599052 3
 
0.8%
4.832399368 2
 
0.5%
5.084322276 1
 
0.3%
0.04579362473 1
 
0.3%
0.1263656343 1
 
0.3%
0.2069376465 1
 
0.3%
0.2875096562 1
 
0.3%
0.368081669 1
 
0.3%
Other values (123) 123
33.6%
ValueCountFrequency (%)
0 227
62.0%
0.02251497004 1
 
0.3%
0.02712027091 1
 
0.3%
0.04579362473 1
 
0.3%
0.1263656343 1
 
0.3%
0.1318343282 1
 
0.3%
0.1621923558 1
 
0.3%
0.2069376465 1
 
0.3%
0.2614307205 1
 
0.3%
0.2875096562 1
 
0.3%
ValueCountFrequency (%)
46.84133418 1
0.3%
44.46157153 1
0.3%
41.99538366 1
0.3%
40.82297285 1
0.3%
38.32062356 1
0.3%
36.97235439 1
0.3%
35.44564088 1
0.3%
34.455494 1
0.3%
33.53810469 1
0.3%
32.51348416 1
0.3%

NIVEL DO RESERVATÓRIO - LT01
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6079105
Minimum1.4588396
Maximum4.255611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T13:00:58.058141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.4588396
5-th percentile2.6591137
Q13.3485832
median3.7397424
Q33.9915299
95-th percentile4.1673674
Maximum4.255611
Range2.7967713
Interquartile range (IQR)0.64294674

Descriptive statistics

Standard deviation0.4843132
Coefficient of variation (CV)0.13423648
Kurtosis1.4114045
Mean3.6079105
Median Absolute Deviation (MAD)0.29286115
Skewness-1.1700151
Sum1320.4952
Variance0.23455927
MonotonicityNot monotonic
2022-12-14T13:00:58.286071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.875286599 1
 
0.3%
4.194900165 1
 
0.3%
3.598548005 1
 
0.3%
4.09392638 1
 
0.3%
3.792635779 1
 
0.3%
3.572079519 1
 
0.3%
4.167379707 1
 
0.3%
4.167330503 1
 
0.3%
3.739502043 1
 
0.3%
4.187361648 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
1.458839649 1
0.3%
1.963316371 1
0.3%
1.975009809 1
0.3%
2.107955938 1
0.3%
2.146511436 1
0.3%
2.21533224 1
0.3%
2.243839602 1
0.3%
2.282457704 1
0.3%
2.353388901 1
0.3%
2.360445827 1
0.3%
ValueCountFrequency (%)
4.255610963 1
0.3%
4.238957544 1
0.3%
4.234671116 1
0.3%
4.22364532 1
0.3%
4.218583852 1
0.3%
4.203865339 1
0.3%
4.201150844 1
0.3%
4.19982486 1
0.3%
4.196092864 1
0.3%
4.194925229 1
0.3%

VAZÃO DE ENTRADA- FT01
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.9386
Minimum22.853874
Maximum301.86282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T13:00:58.527001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum22.853874
5-th percentile67.465472
Q1176.92321
median221.32677
Q3252.28832
95-th percentile279.87289
Maximum301.86282
Range279.00895
Interquartile range (IQR)75.365109

Descriptive statistics

Standard deviation65.822557
Coefficient of variation (CV)0.32275673
Kurtosis0.12273827
Mean203.9386
Median Absolute Deviation (MAD)37.080786
Skewness-1.0284619
Sum74641.528
Variance4332.609
MonotonicityNot monotonic
2022-12-14T13:00:58.765931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
233.0458415 1
 
0.3%
70.89670902 1
 
0.3%
272.5505651 1
 
0.3%
68.22664969 1
 
0.3%
212.9264864 1
 
0.3%
271.7262105 1
 
0.3%
57.28904612 1
 
0.3%
176.8569556 1
 
0.3%
268.0390467 1
 
0.3%
69.14874734 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
22.85387429 1
0.3%
23.67098368 1
0.3%
45.13681835 1
0.3%
45.76276124 1
0.3%
45.82522564 1
0.3%
46.14894212 1
0.3%
46.38032131 1
0.3%
53.48451341 1
0.3%
53.76006223 1
0.3%
55.46804079 1
0.3%
ValueCountFrequency (%)
301.8628197 1
0.3%
292.774423 1
0.3%
291.8711243 1
0.3%
291.7827042 1
0.3%
290.2942263 1
0.3%
286.9505081 1
0.3%
286.6973852 1
0.3%
284.5989176 1
0.3%
284.3868116 1
0.3%
283.8818906 1
0.3%

VAZÃO DE GRAVIDADE - FT02
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.75689
Minimum3.5839761
Maximum181.56548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T13:00:59.039862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.5839761
5-th percentile51.945779
Q187.527397
median122.66958
Q3132.9382
95-th percentile154.80146
Maximum181.56548
Range177.9815
Interquartile range (IQR)45.410808

Descriptive statistics

Standard deviation32.625729
Coefficient of variation (CV)0.29193482
Kurtosis-0.13348699
Mean111.75689
Median Absolute Deviation (MAD)16.460591
Skewness-0.69494434
Sum40903.024
Variance1064.4382
MonotonicityNot monotonic
2022-12-14T13:00:59.280801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101.8270885 1
 
0.3%
61.64856593 1
 
0.3%
153.072231 1
 
0.3%
55.18085019 1
 
0.3%
109.5066519 1
 
0.3%
154.2589423 1
 
0.3%
58.51367188 1
 
0.3%
79.11509991 1
 
0.3%
142.1610578 1
 
0.3%
61.69558716 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
3.58397611 1
0.3%
15.88854376 1
0.3%
33.55565643 1
0.3%
34.30063144 1
0.3%
34.70834827 1
0.3%
35.52933693 1
0.3%
37.73271974 1
0.3%
38.05954901 1
0.3%
38.89107927 1
0.3%
39.44496918 1
0.3%
ValueCountFrequency (%)
181.565478 1
0.3%
177.4906346 1
0.3%
173.0618277 1
0.3%
169.2136116 1
0.3%
166.4727999 1
0.3%
165.6624734 1
0.3%
165.2773012 1
0.3%
165.0567973 1
0.3%
163.15286 1
0.3%
162.393026 1
0.3%

VAZÃO DE RECALQUE - FT03
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.048801
Minimum10.383355
Maximum143.98841
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T13:00:59.532740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10.383355
5-th percentile31.029249
Q173.518915
median105.68828
Q3112.03439
95-th percentile124.80256
Maximum143.98841
Range133.60506
Interquartile range (IQR)38.515479

Descriptive statistics

Standard deviation30.560818
Coefficient of variation (CV)0.33200669
Kurtosis-0.15897889
Mean92.048801
Median Absolute Deviation (MAD)10.116234
Skewness-0.9971956
Sum33689.861
Variance933.96358
MonotonicityNot monotonic
2022-12-14T13:00:59.778673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.51771514 1
 
0.3%
32.02935272 1
 
0.3%
107.9638246 1
 
0.3%
34.73455082 1
 
0.3%
78.11610703 1
 
0.3%
127.0728067 1
 
0.3%
19.22242113 1
 
0.3%
67.13882851 1
 
0.3%
119.3609098 1
 
0.3%
26.08802277 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
10.38335467 1
0.3%
18.78395883 1
0.3%
19.01510628 1
0.3%
19.22242113 1
0.3%
20.09405685 1
0.3%
20.10896885 1
0.3%
20.59290862 1
0.3%
21.63480695 1
0.3%
22.6513927 1
0.3%
22.69457825 1
0.3%
ValueCountFrequency (%)
143.9884148 1
0.3%
141.122522 1
0.3%
140.1231352 1
0.3%
137.250213 1
0.3%
137.2110043 1
0.3%
136.7754704 1
0.3%
136.7724743 1
0.3%
133.4322859 1
0.3%
133.0077931 1
0.3%
131.2276457 1
0.3%

PRESSÃO DE SUCÇÃO - PT01
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5599323
Minimum2.5995952
Maximum5.4753866
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T13:01:00.023601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.5995952
5-th percentile3.5513354
Q14.2243679
median4.6631536
Q35.0149587
95-th percentile5.2617872
Maximum5.4753866
Range2.8757914
Interquartile range (IQR)0.79059089

Descriptive statistics

Standard deviation0.55684561
Coefficient of variation (CV)0.12211708
Kurtosis0.36096531
Mean4.5599323
Median Absolute Deviation (MAD)0.38219037
Skewness-0.81482312
Sum1668.9352
Variance0.31007704
MonotonicityNot monotonic
2022-12-14T13:01:00.246536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.838139951 1
 
0.3%
5.32407101 1
 
0.3%
4.505640248 1
 
0.3%
5.210670809 1
 
0.3%
4.78086713 1
 
0.3%
4.371084839 1
 
0.3%
5.394903402 1
 
0.3%
5.213329951 1
 
0.3%
4.601421118 1
 
0.3%
5.376384755 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
2.599595224 1
0.3%
2.755907903 1
0.3%
2.801692863 1
0.3%
2.920023123 1
0.3%
2.973470807 1
0.3%
3.028406084 1
0.3%
3.110565007 1
0.3%
3.220167051 1
0.3%
3.223515431 1
0.3%
3.298963288 1
0.3%
ValueCountFrequency (%)
5.475386639 1
0.3%
5.469098727 1
0.3%
5.428008278 1
0.3%
5.420319339 1
0.3%
5.40986371 1
0.3%
5.409394821 1
0.3%
5.394903402 1
0.3%
5.376384755 1
0.3%
5.376057903 1
0.3%
5.329075277 1
0.3%

PRESSÃO DE RECALQUE - PT02
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.832988
Minimum0.83083199
Maximum22.693986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T13:01:00.499459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.83083199
5-th percentile3.402025
Q113.197534
median20.196981
Q321.119525
95-th percentile21.80671
Maximum22.693986
Range21.863154
Interquartile range (IQR)7.9219906

Descriptive statistics

Standard deviation6.3239312
Coefficient of variation (CV)0.37568678
Kurtosis0.048187475
Mean16.832988
Median Absolute Deviation (MAD)1.2285623
Skewness-1.2321814
Sum6160.8738
Variance39.992106
MonotonicityNot monotonic
2022-12-14T13:01:00.726393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.27573272 1
 
0.3%
4.375565251 1
 
0.3%
19.17370315 1
 
0.3%
4.513263542 1
 
0.3%
14.23600788 1
 
0.3%
22.10226162 1
 
0.3%
2.476588329 1
 
0.3%
13.96529599 1
 
0.3%
21.46042641 1
 
0.3%
3.198171616 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
0.8308319853 1
0.3%
1.177278982 1
0.3%
1.503833353 1
0.3%
1.619448689 1
0.3%
1.921274748 1
0.3%
2.014124649 1
0.3%
2.069669306 1
0.3%
2.153511247 1
0.3%
2.236963258 1
0.3%
2.331033865 1
0.3%
ValueCountFrequency (%)
22.69398618 1
0.3%
22.42783284 1
0.3%
22.10226162 1
0.3%
22.06411393 1
0.3%
21.9943076 1
0.3%
21.98121619 1
0.3%
21.97036803 1
0.3%
21.9528203 1
0.3%
21.9524219 1
0.3%
21.94964409 1
0.3%

Interactions

2022-12-14T13:00:54.113252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:40.243992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:41.925111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:43.578048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:45.231997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:46.923717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:48.555863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:50.709004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:52.410089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:54.292199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:40.440980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:42.108055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:43.758979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:45.425853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:47.108709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:48.748804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:50.917010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:52.608030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:54.471232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:40.619925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:42.286888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:43.930258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:45.610674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:47.296010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:48.935747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:51.101182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:52.792331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:54.642180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:40.793873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:42.459832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:44.102345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:45.798065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:47.462952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:49.117691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:51.276128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:52.972276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:54.826129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:40.977988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:42.647777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:44.289287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:45.979009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:47.645794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:49.304634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:51.465308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:53.161217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:55.019074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:41.162930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:42.833175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:44.474231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:46.159955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:47.819794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:49.499579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:51.650251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:53.350160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:55.219013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:41.358136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:43.026078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:44.669172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:46.359885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:48.010735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:49.704516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:51.851189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:53.550099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:55.401957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:41.551078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:43.215182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:44.861111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:46.555831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:48.193974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:49.901819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:52.038131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:53.744040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:55.590899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:41.748165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:43.406123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:45.055052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:46.750771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:48.386916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:50.105763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:52.232143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T13:00:53.935980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-14T13:01:00.912320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-14T13:01:01.235459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-14T13:01:01.555115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-14T13:01:01.871023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-14T13:01:02.193511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-14T13:00:55.833824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-14T13:00:56.175720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

FREQUENCIA BOMBA 1FREQUENCIA BOMBA 2FREQUENCIA BOMBA 3NIVEL DO RESERVATÓRIO - LT01VAZÃO DE ENTRADA- FT01VAZÃO DE GRAVIDADE - FT02VAZÃO DE RECALQUE - FT03PRESSÃO DE SUCÇÃO - PT01PRESSÃO DE RECALQUE - PT02
Timestamp
2020-01-0153.90816.2930.0003.875233.046101.82795.5184.83821.276
2020-01-0253.90818.7730.0003.985187.290107.52697.2324.93420.974
2020-01-0353.59219.3070.0003.876204.170108.89298.0054.82520.976
2020-01-0453.96219.2730.0003.926239.731107.74097.8424.87321.733
2020-01-0552.87715.2900.0004.045217.360118.92899.7374.98119.667
2020-01-0649.47924.0884.8323.740174.748125.226107.3354.62721.086
2020-01-0750.52021.2654.3273.369224.250115.595103.7634.27121.119
2020-01-0854.63021.1260.0003.389209.203117.374101.6594.30521.070
2020-01-0954.78819.7900.0003.421235.553121.103104.5984.30320.917
2020-01-1054.65423.5960.0003.405225.445120.157107.2634.29321.250
FREQUENCIA BOMBA 1FREQUENCIA BOMBA 2FREQUENCIA BOMBA 3NIVEL DO RESERVATÓRIO - LT01VAZÃO DE ENTRADA- FT01VAZÃO DE GRAVIDADE - FT02VAZÃO DE RECALQUE - FT03PRESSÃO DE SUCÇÃO - PT01PRESSÃO DE RECALQUE - PT02
Timestamp
2020-12-2214.0397.9040.0003.70053.48563.27337.8284.7944.462
2020-12-2357.08135.9920.0002.545260.923152.083116.4593.37421.444
2020-12-2444.54927.9938.4242.907254.397131.573103.1643.80319.938
2020-12-2552.12519.1970.0003.959244.322113.99892.0104.93119.936
2020-12-2652.32424.5720.0003.883227.834128.669100.8644.81320.579
2020-12-2750.53213.2450.0004.139221.111125.41390.9305.12018.466
2020-12-2852.09227.0430.0004.195241.122129.55199.4535.11920.024
2020-12-2952.19827.2520.0004.013209.976125.79599.2694.94920.155
2020-12-3052.49427.4910.0003.729208.305132.393101.4724.64420.082
2020-12-3145.61827.0937.0883.776226.161124.05297.1044.71420.237